To alleviate the inadequacy of statistical learning methods, deep learning algorithms provide the powerful functionality to represent large-scale raw data for different tasks and thus facilitate the learning of latent patterns in the data [1]}.
Recently, deep learning architectures have produced powerful systems to address several estimation problems related to protein sequences,
such as protein-protein interaction [2]}, protein binding affinity upon mutation [3]}, and protein structural changes [4]} estimation.
These works typically use convolutional neural networks (CNNs) for automatically selecting local features, recurrent neural networks (RNN) that aim at preserving the contextualized and long-term ordering information or the combination of both CNNs and RNNs.
In contrast, fewer efforts have been made to capture the pairwise interactions of the protein drug interaction with deep learning,
which remains a non-trivial problem with the following challenges:
(i) Characterization of the proteins and drugs requires a model to effectively filter and aggregate their local features, while preserving significant contextualized and sequential information of the amino acids and drug fingerprints;
(ii) Constructing a deep neural architecture without biological insights often suffers from the interpretation issue;
(iii)
An effective mechanism is also needed to apprehend the mutual influence of protein-drug pairs in DTI prediction. Moreover,the framework needs to be scalable to large data.
Corresponding methods, including DeepDTI [5]}, RFDT [6]}, DeepDTA [7]} and DeepConv-DTI [8]}, employed deep neural networks on DTI prediction tasks.
Specifically, DeepDTI built by [5]} used the deep belief network (DBN), with features such as the composition of amino acids, dipeptides, and tripeptides for proteins and fingerprints for drugs. RFDT by [6]} employed stacked Auto-Encoder (AE) to abstract original features into a latent representation with a small dimension. With latent representation, they trained a Random Forest (RF), which performed better than previous methods.
DeepDTA [7]} and DeepConv-DTI [8]} both used CNN to extract local residue patterns to predict the binding affinity between drugs and targets. They performed convolution on various lengths of the subsequences' amino acids to capture local residue patterns of generalized protein classes.
